base on LiteRT is the new name for TensorFlow Lite (TFLite). While the name is new, it's still the same trusted, high-performance runtime for on-device AI, now with an expanded vision. # LiteRT Next LiteRT Next is a new set of APIs that improves upon LiteRT, particularly in terms of hardware acceleration and performance for on-device ML and AI applications. The APIs are an alpha release and available in Kotlin and C++. The LiteRT Next CompiledModel API builds on the TensorFlow Lite Interpreter API, and simplifies the model loading and execution process for on-device machine learning. The new APIs provide a new streamlined way to use hardware acceleration, removing the need to deal with model FlatBuffers, I/O buffer interoperability, and delegates. The LiteRT Next APIs are not compatible with the LiteRT APIs. ## Key features LiteRT Next contains the following key benefits and features: - **New LiteRT API:** Streamline development with automated accelerator selection, true async execution, and efficient I/O buffer handling. - **Best-in-class GPU Performance:** Use state-of-the-art GPU acceleration for on-device ML. The new buffer interoperability enables zero-copy and minimizes latency across various GPU buffer types. - **Superior Generative AI inference:** Enable the simplest integration with the best performance for GenAI models. - **Unified NPU Acceleration:** Offer seamless access to NPUs from major chipset providers with a consistent developer experience. LiteRT NPU acceleration is available through an [Early Access Program](https://forms.gle/CoH4jpLwxiEYvDvF6). ## Key improvements LiteRT Next (CompiledModel API) contains the following key improvements on LiteRT (TFLite Interpreter API). For a comprehensive guide to setting up your application with LiteRT Next, see the Get Started guide. - **Accelerator usage:** Running models on GPU with LiteRT requires explicit delegate creation, function calls, and graph modifications. With LiteRT Next, just specify the accelerator. - **Native hardware buffer interoperability:** LiteRT does not provide the option of buffers, and forces all data through CPU memory. With LiteRT Next, you can pass in Android Hardware Buffers (AHWB), OpenCL buffers, OpenGL buffers, or other specialized buffers. - **Async execution:** LiteRT Next comes with a redesigned async API, providing a true async mechanism based on sync fences. This enables faster overall execution times through the use of diverse hardware – like CPUs, GPUs, CPUs, and NPUs – for different tasks. - **Model loading:** LiteRT Next does not require a separate builder step when loading a model. For more details, check our [official documentation](https://ai.google.dev/edge/litert/next/overview). ## Build From Source 1. Start a docker daemon 2. Run [build_with_docker.sh](./build/build_with_docker.sh) under [build/](./build) 3. For more information about how to use docker interactive shell/ building different targets. Please refer to [build/README.md](./build/README.md) ", Assign "at most 3 tags" to the expected json: {"id":"13839","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"